DocumentCode :
2324864
Title :
A novel approach to solve the sparsity problem in collaborative filtering
Author :
Zhou, Jia ; Luo, Tiejian
fYear :
2010
fDate :
10-12 April 2010
Firstpage :
165
Lastpage :
170
Abstract :
Collaborative Filtering (CF) is the most successful approach of Recommender System. Although it has made significant progress over the last decade, the current CF method is stressed by the sparsity problem. In this paper we propose a novel approach to address this issue. Multiple Imputation (MI) is a useful statistic strategy for dealing with data sets with missing values and replace each missing value with a set of plausible values that represent the uncertainty about the right value. In our approach we apply MI technique in the data processing procedure to turn the original sparse data into dense data. And then we use the dense data and the original data in the following CF progress separately. We compare their performance both in cosine-based and correlation-based similarity measures. We conduct a 10-fold cross validation and take the MAE as the evaluation metrics. Our experimental results show that our approach can efficiently solve the extreme sparsity problem, and provide better recommendation results than traditional CF method.
Keywords :
information filtering; recommender systems; statistical analysis; collaborative filtering; correlation-based similarity measures; cosine-based similarity measures; data processing procedure; multiple imputation statistic strategy; recommender system; sparsity problem solving; Books; Collaboration; Collaborative work; Data processing; Information filtering; Information filters; Information retrieval; Recommender systems; Statistics; Uncertainty; Collaborative Filetring; Mutiple Imputaion; Recommender Systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Networking, Sensing and Control (ICNSC), 2010 International Conference on
Conference_Location :
Chicago, IL
Print_ISBN :
978-1-4244-6450-0
Type :
conf
DOI :
10.1109/ICNSC.2010.5461512
Filename :
5461512
Link To Document :
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